This application is related to the following, all of which are incorporated herein by reference in their entirety:
It is well-known that maintenance of software configurations on computers in an enterprise can be difficult. Many times, when modifications are made to existing software configurations, the existing software ceases to operate properly. Furthermore, different computers may have different operating systems (OS's), OS versions and/or OS patch sets. In addition, different computers may have different software applications, application versions and/or application patch sets.
As a result, when it is desired or necessary to perform what is nominally a global software modification—e.g., to install a new application—to minimize or avoid breakage, it becomes necessary to variously adjust the modification procedure to accommodate breakage caused by the various configurations.
A method is employed to group computers to facilitate application of a software modification to the computers. The method includes identifying a global set of computers to which it is desired to apply the software modification. Based on characteristics of software configurations of the computers of the identified global set, the computers of the identified global set are grouped into a plurality of clusters.
Grouping the computers into a plurality of clusters includes processing syntactic information about the computers to identify the plurality of clusters and applying the software modification to the computers of the clusters. The software modification is applied with an adjustment for each cluster in an attempt to avoid software breakage of the computers of that cluster.
a to 4c graphically illustrate example output at various stages of the
As discussed in the Background, due to the disparate configurations of the various computers in an enterprise, software configurations tend to break in different ways when it is attempted to make modifications to those software configurations. Thus, it is desirable to group the computers such that the same adjustment applied to a baseline software modification procedure relative to one computer in a group can be applied to other computers in the same group.
At step 106, a subset of the first “similar host group” is processed. In particular, the hosts of the subset are modified using the baseline software modification procedure. As necessary, the baseline software modification procedure is adjusted, until the hosts of the subset can be validated as successfully modified. Step 106 may be performed in a “mini-loop” such as that shown in
Continuing on, at step 108, the remainder of the first “similar host group” is modified using the baseline software modification procedure, nominally with the adjustment determined in step 106, but further adjusted as appropriate, so that the remainder of the hosts of the first “similar host group” can be validated. Step 108, also, may be performed in a “mini-loop” such as that shown in
At step 110, it is determined if all the hosts of the set (determined in step 102) have been modified (not counting any hosts that were intentionally left unmodified). If not, then, at step 112, a next “similar host group” is selected among the remaining unmodified hosts. For example, the next “similar host group” may be the set of remaining computers that is most similar to the first set, or the largest set of remaining computers that are most similar to each other, or the set that is most dissimilar to the first set. Steps 106, 108 and 110 are repeated for this selected next “similar host group.” If it is determined at step 110 that all the hosts of the set have been modified, processing is complete at step 114.
In the above flow, if at any point we have modified a number of hosts such that either the current grouping is not useful for selecting the next hosts to be modified or a fresh grouping would possibly be more useful, we may optionally restart the grouping on the subset of the original set of hosts that are still unmodified. Furthermore, when a set of hosts is grouped, the grouping may be done multiple times according to different sets of parameters in order to see whether one particular choice of parameters produces a better grouping. As an example, such parameters may include: choosing to include or exclude particular types of semantic info; assigning different weights to different types of semantic or syntactic info; etc.
As a result of the
As mentioned above,
Now, with reference to
With respect to step 302, this step is typically performed manually. Step 302 may also include providing meta-data for each computer that can be used in the segmenting step (step 308, discussed below). The meta-data may include, for example, the function/mission/line of business in which the computer is used or other attributes. The information is typically provided by people. Other meta-data may include an indication of priority/importance—i.e., an indication of which computers, or which applications on which computers, are more important than others.
Other meta-data may include an indication of the “validatability,” which refers to the existence of validation test procedures for the software packages on a computer. For example, a computer that is not very validatable is one for which reliable validation test procedures are not known for its primary software packages and, therefore, cannot be thoroughly tested. By contrast, a very validatable host is one that has software packages for which validation testing has already been done and, therefore, it is known how to thoroughly validate and/or apply breakage workarounds. Intermediately, there are hosts for which there is some confidence that validation testing can be properly performed, but for which not all the validation testing has already been performed.
Generally, the semantic analysis (step 304) clusters the hosts based on properties of the hosts that are known or thought to be significant differentiators with regard to the software modification to be attempted. For example, a significant differentiator may be the operating system and/or software packages installed on each host, including the versions/patch-sets associated with the operating system and/or patch sets.
In general, then, a way is determined to clearly define the state of a computer, and then to define a notion of similarity between such states. One way to do this in a specific implementation is by considering the state of each computer to be a vector of atoms. An atom can represent one or more of, for example, installed OS version/patch; installed SW applications/versions/patch-sets; a computer group, of which the computer is a member (e.g., “accounting” or “web services”); a primary line-of-business of the computer; a flag indicating whether the computer performs a mission-critical task; and a ranking of the “validatability” of the host (an aggregate of “validatability” ranking of applicable atoms).
Other possible attributes of an atom include, for example, a weight to indicate level of importance of that atom; for what the atom indicates, an indication of whether it is of primary or secondary importance to the mission of the host; for what the atom indicates, an indication of variance based on total software inventory; for the software application that the atom indicates, identify validation test suites and/or test procedures (e.g., provided by vendor or developed for customer use during software maintenance cycles); and a ranking of the “validatability” for the software application that the atom indicates.
Referring still to
Step 308 (within step 304) is a segmentation step. The operation of the segmenting step 308 is based at least on information gathered during the semantic inspection step 306. In particular, the population of hosts is segmented according to at least some of the semantic information. For example, the segmenting may be by operating system or by application or application groups. A segment may also be further segmented—e.g., first by operating system and then by application groups. The intent is to start on a segment that is the best starting point for the modification, and this starting segment can be chosen based on size, degree of similarity with the other segments, etc.
In a simple example, the segmenting step may be performed via a simple selection, based on individual computers in the population having or not having some attribute. For example, an operating system of a computer may be characterized by a micro-version. An OS micro-version is a naming representation of the OS name, version, and applied patch-sets (including service packs and similar upgrades) and optionally representing the install order of the patch-sets. An application micro-version is defined similarly.
Micro-versions can provide a fast and accurate similarity measure for a set of computers, and test-equivalence classes can be realized simply by grouping computers with the same OS micro-versions together, and optionally also sub-grouping by application micro-versions. It is thought that this approach works particularly well for a larger host population having some micro-versions that occur frequently, such as in a centrally managed IT dept where operating systems, applications and patch-sets are applied in the same order to many computers.
Turning again to a more general discussion of the segmenting step 308, a segment may be relatively simplistically determined by using one attribute value (e.g., a specific OS “micro-version”), and every computer having that value of the attribute falls within a particular segment, with all other computers falling outside that particular segment. On the other hand, a segment may be relatively complexly determined by providing many attribute values to a cluster-analysis algorithm—for example with inputs being the OS/SW micro-versions or other attributes, and the outputs being the segments (also known as “clusters” with respect to cluster analysis algorithms). Generally, the segmentation of step 308 does not use information gathered in the syntactic inspection step 314.
The result of the segmenting step 308 is one or more segments, and possibly some outliers, which are a subset of “all hosts” (determined in step 302) and represent similar hosts. The value of the segmentation step is that it can minimize or prevent mis-clustering of hosts (e.g., based on the gathered syntactic information from step 314), where it is known from the semantic information that the hosts are in fact very dissimilar and are advantageously not clustered together. An example of this is two computers that are 90% similar, but where the 10% difference actually represents different operating systems and, hence, the computers are fundamentally dissimilar for the purposes of applying the software modification.
As mentioned above, the syntactic analysis (312) is performed on one segment at a time—the segment chosen in step 310. (In some cases, for example, where no segments were identified, then the syntactic analysis (312) is performed on all of the hosts.) Step 314 is an information gathering step, to obtain syntactic information about the hosts of the segment on which the syntactic analysis 312 is being performed. The syntactic information may be present, for example, in an inventory of information about files (and, more generally, containers) accessible by each host. For example, application Ser. No. 11/277,596 describes a method to maintain an inventory of such information for one or more hosts.
In the inventory-comparison analysis step 316 of the syntactic analysis, an indication of a set of clusters is determined. The clusters are “test-equivalence classes” representing hosts deemed to be syntactically similar, based on the syntactic information about the hosts. There may also be some outliers that were determined to not fit into any of the clusters.
In some examples, the semantic analysis step 304 is not performed, with reliance for clustering being entirely on syntactic information in the syntactic analysis step 312. For example, the set of operating system files (or, as appropriate, other files or characteristics that are determined to affect what adjustments to make to the baseline software modification procedure) can be used as the primary “clustering key” in the inventory comparison analysis step 316 of the syntactic analysis step 312. However, it is difficult in many instances to determine which files are operating system files and which files are “other” files. For example, with the Windows operating system, it can be difficult to distinguish between where the operating system ends and the applications start.
On the other hand, the reader may be considering why the syntactic analysis step 312 is employed at all, after the segmentation output of the semantic analysis step 304. In general, as discussed in the previous paragraph, the information that most affects the adjustments to make to the baseline software modification procedure is the syntactic information. In general, however, the semantic analysis step 304 is helpful, for example, to minimize the occurrence of mis-clustering of computers that are in fact very dissimilar. Finally, at step 318, a set of one or more similar host groups is provided.
a, 4b and 4c graphically illustrate example output at various stages of the
It is noted, with reference to
In summary, then, by intelligently grouping computers in a manner that is likely to correlate to the manner in which a baseline software modification procedure may be adjusted to avoid breakage, administration of a software modification to computers of an enterprise may be simplified.
Number | Name | Date | Kind |
---|---|---|---|
4688169 | Joshi | Aug 1987 | A |
4982430 | Frezza et al. | Jan 1991 | A |
5155847 | Kirouac et al. | Oct 1992 | A |
5222134 | Waite et al. | Jun 1993 | A |
5390314 | Swanson | Feb 1995 | A |
5521849 | Adelson et al. | May 1996 | A |
5560008 | Johnson et al. | Sep 1996 | A |
5699513 | Feigen et al. | Dec 1997 | A |
5778349 | Okonogi | Jul 1998 | A |
5787427 | Benantar et al. | Jul 1998 | A |
5842017 | Hookway et al. | Nov 1998 | A |
5907709 | Cantey et al. | May 1999 | A |
5974149 | Leppek | Oct 1999 | A |
5987610 | Franczek et al. | Nov 1999 | A |
5987611 | Freund | Nov 1999 | A |
5991881 | Conklin et al. | Nov 1999 | A |
6073142 | Geiger et al. | Jun 2000 | A |
6192401 | Modiri et al. | Feb 2001 | B1 |
6192475 | Wallace | Feb 2001 | B1 |
6256773 | Bowman-Amuah | Jul 2001 | B1 |
6275938 | Bond et al. | Aug 2001 | B1 |
6356957 | Sanchez, II et al. | Mar 2002 | B2 |
6393465 | Leeds | May 2002 | B2 |
6442686 | McArdle et al. | Aug 2002 | B1 |
6449040 | Fujita | Sep 2002 | B1 |
6453468 | D'Souza | Sep 2002 | B1 |
6460050 | Pace et al. | Oct 2002 | B1 |
6662219 | Nishanov et al. | Dec 2003 | B1 |
6748534 | Gryaznov et al. | Jun 2004 | B1 |
6769008 | Kumar et al. | Jul 2004 | B1 |
6769115 | Oldman | Jul 2004 | B1 |
6795966 | Lim et al. | Sep 2004 | B1 |
6832227 | Seki et al. | Dec 2004 | B2 |
6834301 | Hanchett | Dec 2004 | B1 |
6847993 | Novaes et al. | Jan 2005 | B1 |
6907600 | Neiger et al. | Jun 2005 | B2 |
6930985 | Rathi et al. | Aug 2005 | B1 |
6934755 | Saulpaugh et al. | Aug 2005 | B1 |
6988101 | Ham et al. | Jan 2006 | B2 |
7010796 | Strom et al. | Mar 2006 | B1 |
7024548 | O'Toole, Jr. | Apr 2006 | B1 |
7039949 | Cartmell et al. | May 2006 | B2 |
7069330 | McArdle et al. | Jun 2006 | B1 |
7082456 | Mani-Meitav et al. | Jul 2006 | B2 |
7093239 | van der Made | Aug 2006 | B1 |
7139916 | Billingsley et al. | Nov 2006 | B2 |
7152148 | Williams et al. | Dec 2006 | B2 |
7251655 | Kaler et al. | Jul 2007 | B2 |
7290266 | Gladstone et al. | Oct 2007 | B2 |
7346781 | Cowle et al. | Mar 2008 | B2 |
7350204 | Lambert et al. | Mar 2008 | B2 |
7353501 | Tang et al. | Apr 2008 | B2 |
7363022 | Whelan et al. | Apr 2008 | B2 |
7370360 | van der Made | May 2008 | B2 |
7441265 | Staamann et al. | Oct 2008 | B2 |
7464408 | Shah et al. | Dec 2008 | B1 |
7506155 | Stewart et al. | Mar 2009 | B1 |
7506170 | Finnegan | Mar 2009 | B2 |
7546333 | Alon et al. | Jun 2009 | B2 |
7546594 | McGuire et al. | Jun 2009 | B2 |
7607170 | Chesla | Oct 2009 | B2 |
7657599 | Smith | Feb 2010 | B2 |
7669195 | Qumei | Feb 2010 | B1 |
7685635 | Vega et al. | Mar 2010 | B2 |
7698744 | Fanton et al. | Apr 2010 | B2 |
7703090 | Napier et al. | Apr 2010 | B2 |
7757269 | Roy-Chowdhury et al. | Jul 2010 | B1 |
7765538 | Zweifel et al. | Jul 2010 | B2 |
7809704 | Surendran et al. | Oct 2010 | B2 |
7823148 | Deshpande et al. | Oct 2010 | B2 |
7836504 | Ray et al. | Nov 2010 | B2 |
7908653 | Brickell et al. | Mar 2011 | B2 |
7937455 | Saha et al. | May 2011 | B2 |
8015563 | Araujo et al. | Sep 2011 | B2 |
20020002704 | Davis et al. | Jan 2002 | A1 |
20020069367 | Tindal et al. | Jun 2002 | A1 |
20020083175 | Afek et al. | Jun 2002 | A1 |
20020099671 | Crosbie et al. | Jul 2002 | A1 |
20030014667 | Kolichtchak | Jan 2003 | A1 |
20030023736 | Abkemeier | Jan 2003 | A1 |
20030033510 | Dice | Feb 2003 | A1 |
20030037094 | Douceur et al. | Feb 2003 | A1 |
20030073894 | Chiang et al. | Apr 2003 | A1 |
20030074552 | Olkin et al. | Apr 2003 | A1 |
20030110280 | Hinchliffe et al. | Jun 2003 | A1 |
20030120601 | Ouye et al. | Jun 2003 | A1 |
20030120811 | Hanson et al. | Jun 2003 | A1 |
20030120935 | Teal et al. | Jun 2003 | A1 |
20030145232 | Poletto et al. | Jul 2003 | A1 |
20030163718 | Johnson et al. | Aug 2003 | A1 |
20030167399 | Audebert et al. | Sep 2003 | A1 |
20030221190 | Deshpande et al. | Nov 2003 | A1 |
20040003258 | Billingsley et al. | Jan 2004 | A1 |
20040015554 | Wilson | Jan 2004 | A1 |
20040051736 | Daniell | Mar 2004 | A1 |
20040054928 | Hall | Mar 2004 | A1 |
20040143749 | Tajalli et al. | Jul 2004 | A1 |
20040167906 | Smith et al. | Aug 2004 | A1 |
20040230963 | Rothman et al. | Nov 2004 | A1 |
20040243678 | Smith et al. | Dec 2004 | A1 |
20040255161 | Cavanaugh | Dec 2004 | A1 |
20050018651 | Yan et al. | Jan 2005 | A1 |
20050086047 | Uchimoto et al. | Apr 2005 | A1 |
20050097147 | Hunt et al. | May 2005 | A1 |
20050108516 | Balzer et al. | May 2005 | A1 |
20050108562 | Khazan et al. | May 2005 | A1 |
20050114672 | Duncan et al. | May 2005 | A1 |
20050132346 | Tsantilis | Jun 2005 | A1 |
20050228990 | Kato et al. | Oct 2005 | A1 |
20050235360 | Pearson | Oct 2005 | A1 |
20050257207 | Blumfield et al. | Nov 2005 | A1 |
20050260996 | Groenendaal | Nov 2005 | A1 |
20050262558 | Usov | Nov 2005 | A1 |
20050273858 | Zadok et al. | Dec 2005 | A1 |
20050283823 | Okajo et al. | Dec 2005 | A1 |
20050289071 | Goin et al. | Dec 2005 | A1 |
20050289538 | Black-Ziegelbein et al. | Dec 2005 | A1 |
20060004875 | Baron et al. | Jan 2006 | A1 |
20060015501 | Sanamrad et al. | Jan 2006 | A1 |
20060036591 | Gerasoulis et al. | Feb 2006 | A1 |
20060037016 | Saha et al. | Feb 2006 | A1 |
20060080656 | Cain et al. | Apr 2006 | A1 |
20060085785 | Garrett | Apr 2006 | A1 |
20060101277 | Meenan et al. | May 2006 | A1 |
20060133223 | Nakamura et al. | Jun 2006 | A1 |
20060136910 | Brickell et al. | Jun 2006 | A1 |
20060136911 | Robinson et al. | Jun 2006 | A1 |
20060195906 | Jin et al. | Aug 2006 | A1 |
20060236398 | Trakic et al. | Oct 2006 | A1 |
20070011746 | Malpani et al. | Jan 2007 | A1 |
20070039049 | Kupferman et al. | Feb 2007 | A1 |
20070050764 | Traut | Mar 2007 | A1 |
20070074199 | Schoenberg | Mar 2007 | A1 |
20070083522 | Nord et al. | Apr 2007 | A1 |
20070101435 | Konanka et al. | May 2007 | A1 |
20070136579 | Levy et al. | Jun 2007 | A1 |
20070169079 | Keller et al. | Jul 2007 | A1 |
20070192329 | Croft et al. | Aug 2007 | A1 |
20070220061 | Tirosh et al. | Sep 2007 | A1 |
20070220507 | Back et al. | Sep 2007 | A1 |
20070253430 | Minami et al. | Nov 2007 | A1 |
20070271561 | Winner et al. | Nov 2007 | A1 |
20070300215 | Bardsley | Dec 2007 | A1 |
20080005737 | Saha et al. | Jan 2008 | A1 |
20080005798 | Ross | Jan 2008 | A1 |
20080010304 | Vempala et al. | Jan 2008 | A1 |
20080034416 | Kumar et al. | Feb 2008 | A1 |
20080052468 | Speirs et al. | Feb 2008 | A1 |
20080082977 | Araujo et al. | Apr 2008 | A1 |
20080120499 | Zimmer et al. | May 2008 | A1 |
20080163207 | Reumann et al. | Jul 2008 | A1 |
20080163210 | Bowman et al. | Jul 2008 | A1 |
20080184373 | Traut et al. | Jul 2008 | A1 |
20080294703 | Craft et al. | Nov 2008 | A1 |
20080301770 | Kinder | Dec 2008 | A1 |
20090038017 | Durham et al. | Feb 2009 | A1 |
20090043993 | Ford et al. | Feb 2009 | A1 |
20090144300 | Chatley et al. | Jun 2009 | A1 |
20090150639 | Ohata | Jun 2009 | A1 |
20090249438 | Litvin et al. | Oct 2009 | A1 |
20100071035 | Budko et al. | Mar 2010 | A1 |
20100100970 | Chowdhury et al. | Apr 2010 | A1 |
20100114825 | Siddegowda | May 2010 | A1 |
20100281133 | Brendel | Nov 2010 | A1 |
20110035423 | Kobayashi et al. | Feb 2011 | A1 |
Number | Date | Country |
---|---|---|
1 482 394 | Dec 2004 | EP |
2 037 657 | Mar 2009 | EP |
WO 9844404 | Oct 1998 | WO |
WO 0184285 | Nov 2001 | WO |
WO 2006012197 | Feb 2006 | WO |
WO 2006124832 | Nov 2006 | WO |
WO 2008054997 | May 2008 | WO |
WO 2011059877 | May 2011 | WO |
WO 2012015485 | Feb 2012 | WO |
WO 2012154890 | Feb 2012 | WO |